AIOps Specialist

Education and Work Experience Requirements:

Bachelor s or Master s degree in Computer Science, Engineering, Data Science, or a related field.

5+ years of experience in MLOps with at least 2 years working specifically with LLMOps pipelines for large language models.

Proven experience in deploying, managing, and optimizing machine learning models in cloud environments at scale.

Experience with AI and Generative AI technologies is a plus. Strong problem-solving abilities and a passion for building scalable, production-grade AI systems. Lead the design and implementation of end-to-end MLOps and LLMOps pipelines, from model development and training to deployment and continuous monitoring in production environments. Develop automation for model deployment and management, ensuring scalability, efficiency, and cost-effectiveness, particularly for large language models (LLMs) in cloud environments. Implement and manage CI/CD pipelines for both ML and LLM models, integrating them seamlessly into the production environment. Collaborate with data scientists, ML engineers, and software developers to ensure the integration of LLMs and ML models into scalable, production-ready systems. Build data pipelines and workflows for training and serving models, ensuring efficient data processing and pipeline orchestration using tools like Kubeflow, Airflow, or MLflow. Create robust monitoring systems for tracking model performance, diagnosing issues, and ensuring the reliability and health of both traditional ML and LLM-based systems. Optimize the performance of deployed models, focusing on cost optimization, speed, and accuracy, including hardware acceleration (GPUs, TPUs). Maintain version control, governance, and model lifecycle management for both ML and LLM models using industry-standard tools. Stay up-to-date with the latest advancements in MLOps, LLMOps, and Generative AI, implementing best practices and innovative approaches to improve the deployment lifecycle. Mandatory Skills: MLOps & LLMOps Expertise: Solid understanding of MLOps best practices and LLMOps workflows for large language models, ensuring seamless integration of AI systems into production environments. Cloud Infrastructure & Deployment: Extensive experience with cloud platforms (AWS, GCP, Azure) for deploying and scaling ML/LLM solutions. Expertise in containerization (Docker, Kubernetes) for model deployment and orchestration. CI/CD Pipelines & Automation: Proficiency in developing and managing CI/CD pipelines for machine learning models using tools like Jenkins, GitLab CI, or CircleCI, and automation tools like Terraform or Ansible. Model Serving & Monitoring: Hands-on experience deploying models using frameworks such as TensorFlow Serving, TorchServe, Kubeflow, or MLflow. Strong understanding of model monitoring and alerting (e. g. , Prometheus, Grafana). Machine Learning Frameworks: Strong knowledge of popular machine learning frameworks like TensorFlow, PyTorch, Hugging Face, and others, with a particular focus on deploying and fine-tuning large language models (e. g. , GPT, BERT). Data Engineering & Pipelines: Expertise in building and managing robust data pipelines using tools like Apache Kafka, Apache Airflow, or Apache Beam to support ML and LLM workflows. Performance Optimization: Ability to optimize the performance of AI models, including working with distributed systems, model compression, and hardware acceleration (GPUs, TPUs). Security & Governance: Deep understanding of security and governance best practices for AI systems, including model versioning, compliance with privacy regulations (GDPR), and ethical AI frameworks. Programming & Scripting: Strong programming skills in Python (for automation, scripting, data manipulation), as well as familiarity with other languages like Java or Scala for pipeline development. Collaboration & Communication: Excellent communication skills to work effectively with cross-functional teams, including data scientists, software engineers, and business stakeholders.